{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:03.374786Z",
     "start_time": "2017-07-24T01:21:02.723564Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "from scipy import stats\n",
    "from IPython.display import display, HTML\n",
    "from sklearn import metrics as me\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option(\"display.max_rows\",20)\n",
    "pd.set_option('precision', 4)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:03.447587Z",
     "start_time": "2017-07-24T01:21:03.376242Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    np.set_printoptions(precision=4)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        #print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        #print('Confusion matrix, without normalization')\n",
    "        pass\n",
    "    \n",
    "    #print(cm)\n",
    "\n",
    "    label = [[\"\\n True Negative\", \"\\n False Positive \\n Type II Error\"],\n",
    "             [\"\\n False Negative \\n Type I Error\", \"\\n True Positive\"]\n",
    "            ]\n",
    "    \n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        \n",
    "        plt.text(j, i, \"{} {}\".format(cm[i, j].round(4), label[i][j]),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "def plot(actual_value, pred_value):\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "\n",
    "    cm_2labels = confusion_matrix(y_pred = pred_value, y_true = actual_value)\n",
    "    plt.figure(figsize=[6,6])\n",
    "    plot_confusion_matrix(cm_2labels, ['Normal', 'Attack'], normalize = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:03.853990Z",
     "start_time": "2017-07-24T01:21:03.449076Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "def evaluate_lstm(model, past_scores, past_scores_20, predictions, predictions_, predictions_20, predictions_20_):\n",
    "    return evaluate(model, past_scores, past_scores_20, predictions, predictions_, predictions_20, predictions_20_, 'LSTM')\n",
    "\n",
    "def display_and_save(name, plot):\n",
    "    fig = plot.get_figure()\n",
    "    fig.savefig(\"result_plots/{}.eps\".format(name.replace(\":\",\"\").strip()), format='eps', dpi=1000)\n",
    "    fig.savefig(\"result_plots/{}.png\".format(name.replace(\":\",\"\").strip()), format='png', dpi=1000)\n",
    "    display(plot)\n",
    "\n",
    "def evaluate(model, past_scores, past_scores_20, predictions, predictions_, predictions_20, predictions_20_, model_type='AE'):\n",
    "    all_scenarios = pd.DataFrame(columns=['Scenarios', 'Number of Features', 'Accuracy', 'F1 Score', 'Precision', 'Recall'])\n",
    "    \n",
    "    def get_best_df(past_scores):\n",
    "        psg = past_scores.sort_values(by='f1_score', ascending=False).groupby(by=['no_of_features', 'hidden_layers'])\n",
    "        df = psg.first().sort_values(by='f1_score', ascending=False)\n",
    "        return df\n",
    "    \n",
    "    def get_result(past_scores):\n",
    "            \n",
    "        df = get_best_df(past_scores)\n",
    "        \n",
    "        #epoch_nof_hidden\n",
    "        key = int(df.iloc[0]['epoch'])\n",
    "        nof = int(df.iloc[0].name[0])\n",
    "        hidden = int(df.iloc[0].name[1])\n",
    "        \n",
    "        return \"{}_{}_{}\".format(key, nof, hidden), nof, df\n",
    "\n",
    "    def view_data(name, past_scores):\n",
    "        _, _, df = get_result(past_scores)\n",
    "        #display(name)\n",
    "        #display(df)\n",
    "        \n",
    "        group_by = 'no_of_features'\n",
    "        if(model_type == 'LSTM'):\n",
    "            group_by = 'hidden_layers'\n",
    "        df1 = df.reset_index().sort_values(by='f1_score', ascending=False).groupby(by=[group_by])\n",
    "        \n",
    "        df1 = df1.first().loc[:,['f1_score', 'f1_score_20', 'time_taken']]\n",
    "        df1 = df1.rename(index={1:\"One\", 12:\"10%\", 24:\"20%\", 48:\"40%\", 122:\"All\"})\n",
    "        df1 = df1.rename(columns={\"f1_score\":\"F1(Test+)\", \"f1_score_20\":\"F1(Test-)\", \"time_taken\":\"Duration(secs)\"})\n",
    "        plot = df1.plot(secondary_y = 'Duration(secs)', title=name)#,figsize=(10, 10))\n",
    "        display_and_save(name, plot)\n",
    "    \n",
    "        \n",
    "    #display(\"Individual Results for each Scenario\")    \n",
    "    view_data(\"Results for {} Train+\".format(model),past_scores)\n",
    "    view_data(\"Results for {} Train-\".format(model),past_scores_20)\n",
    "        \n",
    "    def get_score(y_true, y_pred):\n",
    "        f1 = me.f1_score(y_true, y_pred)\n",
    "        pre = me.precision_score(y_true, y_pred)\n",
    "        rec = me.recall_score(y_true, y_pred)\n",
    "        acc = me.accuracy_score(y_true, y_pred)\n",
    "        return {\"F1 Score\":f1, \"Precision\":pre, \"Recall\":rec, \"Accuracy\":acc}\n",
    "    \n",
    "    display(\"Combined Results from all Scenarios for {}\".format(model))\n",
    "\n",
    "    \n",
    "    def accumulate_scenarios(predictions, past_scores):\n",
    "        key, nof, df = get_result(past_scores)\n",
    "        y_true = predictions[key][\"Actual\"]\n",
    "        y_pred = predictions[key][\"Prediction\"]\n",
    "        scores = get_score(y_true, y_pred)\n",
    "        scores.update({\"Model\":model,\"Scenarios\":scenario,\"Number of Features\":nof})\n",
    "        \n",
    "        return pd.DataFrame(scores, index=[1])\n",
    "    \n",
    "    scenario = \"Train+_Test+\"\n",
    "    all_scenarios = all_scenarios.append(accumulate_scenarios(predictions, past_scores))\n",
    "    \n",
    "    scenario = \"Train+_Test-\"\n",
    "    all_scenarios = all_scenarios.append(accumulate_scenarios(predictions_, past_scores))\n",
    "    \n",
    "    scenario = \"Train-_Test+\"\n",
    "    all_scenarios = all_scenarios.append(accumulate_scenarios(predictions_20, past_scores_20))\n",
    "    \n",
    "    scenario = \"Train-_Test-\"\n",
    "    all_scenarios = all_scenarios.append(accumulate_scenarios(predictions_20_, past_scores_20))\n",
    "    \n",
    "    \n",
    "    display(all_scenarios.set_index(['Model','Scenarios','Number of Features']))\n",
    "    \n",
    "    return all_scenarios\n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:03.962117Z",
     "start_time": "2017-07-24T01:21:03.855592Z"
    }
   },
   "outputs": [],
   "source": [
    "past_scores = pd.read_pickle(\"dataset/scores/tf_dense_only_nsl_kdd_scores_all.pkl\")\n",
    "past_scores_20 = pd.read_pickle(\"dataset/scores/tf_dense_only_nsl_kdd_scores_all-.pkl\")\n",
    "predictions = pd.read_pickle(\"dataset/tf_dense_only_nsl_kdd_predictions.pkl\")\n",
    "predictions_ = pd.read_pickle(\"dataset/tf_dense_only_nsl_kdd_predictions__.pkl\")\n",
    "predictions_20 = pd.read_pickle(\"dataset/tf_dense_only_nsl_kdd_predictions-.pkl\")\n",
    "predictions_20_ = pd.read_pickle(\"dataset/tf_dense_only_nsl_kdd_predictions-__.pkl\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:12.166210Z",
     "start_time": "2017-07-24T01:21:03.963646Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb01b391470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb01af4c940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Combined Results from all Scenarios for Fully Connected'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th>Scenarios</th>\n",
       "      <th>Number of Features</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Fully Connected</th>\n",
       "      <th>Train+_Test+</th>\n",
       "      <th>48</th>\n",
       "      <td>0.8670</td>\n",
       "      <td>0.8739</td>\n",
       "      <td>0.9490</td>\n",
       "      <td>0.8098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train+_Test-</th>\n",
       "      <th>48</th>\n",
       "      <td>0.7576</td>\n",
       "      <td>0.8350</td>\n",
       "      <td>0.9424</td>\n",
       "      <td>0.7495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train-_Test+</th>\n",
       "      <th>48</th>\n",
       "      <td>0.8561</td>\n",
       "      <td>0.8695</td>\n",
       "      <td>0.8988</td>\n",
       "      <td>0.8420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train-_Test-</th>\n",
       "      <th>48</th>\n",
       "      <td>0.7504</td>\n",
       "      <td>0.8396</td>\n",
       "      <td>0.8856</td>\n",
       "      <td>0.7981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Accuracy  F1 Score  \\\n",
       "Model           Scenarios    Number of Features                       \n",
       "Fully Connected Train+_Test+ 48                    0.8670    0.8739   \n",
       "                Train+_Test- 48                    0.7576    0.8350   \n",
       "                Train-_Test+ 48                    0.8561    0.8695   \n",
       "                Train-_Test- 48                    0.7504    0.8396   \n",
       "\n",
       "                                                 Precision  Recall  \n",
       "Model           Scenarios    Number of Features                     \n",
       "Fully Connected Train+_Test+ 48                     0.9490  0.8098  \n",
       "                Train+_Test- 48                     0.9424  0.7495  \n",
       "                Train-_Test+ 48                     0.8988  0.8420  \n",
       "                Train-_Test- 48                     0.8856  0.7981  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_scenarios_fcn = evaluate(\"Fully Connected\", past_scores, past_scores_20, predictions, predictions_, predictions_20, predictions_20_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:12.220035Z",
     "start_time": "2017-07-24T01:21:12.167645Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "past_scores = pd.read_pickle(\"dataset/scores/tf_vae_dense_trained_together_nsl_kdd_all.pkl\")\n",
    "past_scores_20 = pd.read_pickle(\"dataset/scores/tf_vae_dense_trained_together_nsl_kdd_all-.pkl\")\n",
    "predictions = pd.read_pickle(\"dataset/tf_vae_dense_trained_together_nsl_kdd_predictions.pkl\")\n",
    "predictions_ = pd.read_pickle(\"dataset/tf_vae_dense_trained_together_nsl_kdd_predictions__.pkl\")\n",
    "predictions_20 = pd.read_pickle(\"dataset/tf_vae_dense_trained_together_nsl_kdd_predictions.pkl\")\n",
    "predictions_20_ = pd.read_pickle(\"dataset/tf_vae_dense_trained_together_nsl_kdd_predictions-__.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:20.562968Z",
     "start_time": "2017-07-24T01:21:12.221818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb01ae72828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb01adab3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Combined Results from all Scenarios for VAE-Softmax'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th>Scenarios</th>\n",
       "      <th>Number of Features</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">VAE-Softmax</th>\n",
       "      <th>Train+_Test+</th>\n",
       "      <th>122</th>\n",
       "      <td>0.8948</td>\n",
       "      <td>0.9036</td>\n",
       "      <td>0.9441</td>\n",
       "      <td>0.8665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train+_Test-</th>\n",
       "      <th>122</th>\n",
       "      <td>0.8173</td>\n",
       "      <td>0.8814</td>\n",
       "      <td>0.9402</td>\n",
       "      <td>0.8296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train-_Test+</th>\n",
       "      <th>48</th>\n",
       "      <td>0.7195</td>\n",
       "      <td>0.6942</td>\n",
       "      <td>0.9151</td>\n",
       "      <td>0.5592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train-_Test-</th>\n",
       "      <th>48</th>\n",
       "      <td>0.8015</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>0.9373</td>\n",
       "      <td>0.8118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Accuracy  F1 Score  Precision  \\\n",
       "Model       Scenarios    Number of Features                                  \n",
       "VAE-Softmax Train+_Test+ 122                   0.8948    0.9036     0.9441   \n",
       "            Train+_Test- 122                   0.8173    0.8814     0.9402   \n",
       "            Train-_Test+ 48                    0.7195    0.6942     0.9151   \n",
       "            Train-_Test- 48                    0.8015    0.8700     0.9373   \n",
       "\n",
       "                                             Recall  \n",
       "Model       Scenarios    Number of Features          \n",
       "VAE-Softmax Train+_Test+ 122                 0.8665  \n",
       "            Train+_Test- 122                 0.8296  \n",
       "            Train-_Test+ 48                  0.5592  \n",
       "            Train-_Test- 48                  0.8118  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_scenarios_vae_sm = evaluate(\"VAE-Softmax\", past_scores, past_scores_20, predictions, predictions_, predictions_20, predictions_20_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:20.619633Z",
     "start_time": "2017-07-24T01:21:20.564478Z"
    }
   },
   "outputs": [],
   "source": [
    "past_scores = pd.read_pickle(\"dataset/scores/tf_vae_only_nsl_kdd_all.pkl\")\n",
    "past_scores_20 = pd.read_pickle(\"dataset/scores/tf_vae_only_nsl_kdd_all-.pkl\")\n",
    "predictions = pd.read_pickle(\"dataset/tf_vae_only_nsl_kdd_predictions.pkl\")\n",
    "predictions_ = pd.read_pickle(\"dataset/tf_vae_only_nsl_kdd_predictions__.pkl\")\n",
    "predictions_20 = pd.read_pickle(\"dataset/tf_vae_only_nsl_kdd_predictions-.pkl\")\n",
    "predictions_20_ = pd.read_pickle(\"dataset/tf_vae_only_nsl_kdd_predictions-__.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:28.910894Z",
     "start_time": "2017-07-24T01:21:20.621201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb01ad6dba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb0193d7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Combined Results from all Scenarios for VAE-GenerateLabels'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th>Scenarios</th>\n",
       "      <th>Number of Features</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">VAE-GenerateLabels</th>\n",
       "      <th>Train+_Test+</th>\n",
       "      <th>1</th>\n",
       "      <td>0.5692</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.5692</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train+_Test-</th>\n",
       "      <th>1</th>\n",
       "      <td>0.8184</td>\n",
       "      <td>0.9001</td>\n",
       "      <td>0.8184</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train-_Test+</th>\n",
       "      <th>1</th>\n",
       "      <td>0.5692</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.5692</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train-_Test-</th>\n",
       "      <th>1</th>\n",
       "      <td>0.8184</td>\n",
       "      <td>0.9001</td>\n",
       "      <td>0.8184</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Accuracy  F1 Score  \\\n",
       "Model              Scenarios    Number of Features                       \n",
       "VAE-GenerateLabels Train+_Test+ 1                     0.5692    0.7255   \n",
       "                   Train+_Test- 1                     0.8184    0.9001   \n",
       "                   Train-_Test+ 1                     0.5692    0.7255   \n",
       "                   Train-_Test- 1                     0.8184    0.9001   \n",
       "\n",
       "                                                    Precision  Recall  \n",
       "Model              Scenarios    Number of Features                     \n",
       "VAE-GenerateLabels Train+_Test+ 1                      0.5692     1.0  \n",
       "                   Train+_Test- 1                      0.8184     1.0  \n",
       "                   Train-_Test+ 1                      0.5692     1.0  \n",
       "                   Train-_Test- 1                      0.8184     1.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_scenarios_vae = evaluate(\"VAE-GenerateLabels\", past_scores, past_scores_20, predictions, predictions_, predictions_20, predictions_20_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:28.926102Z",
     "start_time": "2017-07-24T01:21:28.912360Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "past_scores = pd.read_pickle(\"dataset/scores/tf_lstm_nsl_kdd-orig_all.pkl\")\n",
    "past_scores_20 = pd.read_pickle(\"dataset/scores/tf_lstm_nsl_kdd-orig_all-.pkl\")\n",
    "predictions = pd.read_pickle(\"dataset/tf_lstm_nsl_kdd_predictions.pkl\")\n",
    "predictions_ = pd.read_pickle(\"dataset/tf_lstm_nsl_kdd_predictions__.pkl\")\n",
    "predictions_20 = pd.read_pickle(\"dataset/tf_lstm_nsl_kdd_predictions-.pkl\")\n",
    "predictions_20_ = pd.read_pickle(\"dataset/tf_lstm_nsl_kdd_predictions-__.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:37.412656Z",
     "start_time": "2017-07-24T01:21:28.927557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb0192b45c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb01ac445c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Combined Results from all Scenarios for LSTM'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>F1 Score</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th>Scenarios</th>\n",
       "      <th>Number of Features</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">LSTM</th>\n",
       "      <th>Train+_Test+</th>\n",
       "      <th>1</th>\n",
       "      <td>0.9949</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>0.9915</td>\n",
       "      <td>0.9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train+_Test-</th>\n",
       "      <th>1</th>\n",
       "      <td>0.9949</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>0.9915</td>\n",
       "      <td>0.9995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train-_Test+</th>\n",
       "      <th>1</th>\n",
       "      <td>0.9992</td>\n",
       "      <td>0.9993</td>\n",
       "      <td>0.9985</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train-_Test-</th>\n",
       "      <th>1</th>\n",
       "      <td>0.9992</td>\n",
       "      <td>0.9993</td>\n",
       "      <td>0.9985</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Accuracy  F1 Score  Precision  Recall\n",
       "Model Scenarios    Number of Features                                       \n",
       "LSTM  Train+_Test+ 1                     0.9949    0.9955     0.9915  0.9995\n",
       "      Train+_Test- 1                     0.9949    0.9955     0.9915  0.9995\n",
       "      Train-_Test+ 1                     0.9992    0.9993     0.9985  1.0000\n",
       "      Train-_Test- 1                     0.9992    0.9993     0.9985  1.0000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_scenarios_lstm = evaluate_lstm(\"LSTM\", past_scores, past_scores_20, predictions, predictions_, predictions_20, predictions_20_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:37.420219Z",
     "start_time": "2017-07-24T01:21:37.414412Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_scenarios = pd.concat([all_scenarios_fcn, all_scenarios_vae_sm, all_scenarios_vae, all_scenarios_lstm],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:37.441658Z",
     "start_time": "2017-07-24T01:21:37.421664Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Model</th>\n",
       "      <th>Fully Connected</th>\n",
       "      <th>LSTM</th>\n",
       "      <th>VAE-GenerateLabels</th>\n",
       "      <th>VAE-Softmax</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scenarios</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Train+_Test+</th>\n",
       "      <td>0.8739</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.9036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train+_Test-</th>\n",
       "      <td>0.8350</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>0.9001</td>\n",
       "      <td>0.8814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train-_Test+</th>\n",
       "      <td>0.8695</td>\n",
       "      <td>0.9993</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.6942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Train-_Test-</th>\n",
       "      <td>0.8396</td>\n",
       "      <td>0.9993</td>\n",
       "      <td>0.9001</td>\n",
       "      <td>0.8700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Model         Fully Connected    LSTM  VAE-GenerateLabels  VAE-Softmax\n",
       "Scenarios                                                             \n",
       "Train+_Test+           0.8739  0.9955              0.7255       0.9036\n",
       "Train+_Test-           0.8350  0.9955              0.9001       0.8814\n",
       "Train-_Test+           0.8695  0.9993              0.7255       0.6942\n",
       "Train-_Test-           0.8396  0.9993              0.9001       0.8700"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_scenarios_display = all_scenarios.loc[:,[ 'Scenarios', 'F1 Score', 'Model']]\n",
    "all_scenarios_pivot = all_scenarios_display.pivot_table('F1 Score', 'Scenarios', 'Model')\n",
    "all_scenarios_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:46.290997Z",
     "start_time": "2017-07-24T01:21:37.442969Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb01abdc6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_and_save(\"All Results with Train_Test in X-axis\",all_scenarios_pivot.plot(kind='bar', figsize=[15,4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:46.311273Z",
     "start_time": "2017-07-24T01:21:46.292417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Scenarios</th>\n",
       "      <th>Train+_Test+</th>\n",
       "      <th>Train+_Test-</th>\n",
       "      <th>Train-_Test+</th>\n",
       "      <th>Train-_Test-</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fully Connected</th>\n",
       "      <td>0.8739</td>\n",
       "      <td>0.8350</td>\n",
       "      <td>0.8695</td>\n",
       "      <td>0.8396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LSTM</th>\n",
       "      <td>0.9955</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>0.9993</td>\n",
       "      <td>0.9993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VAE-GenerateLabels</th>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.9001</td>\n",
       "      <td>0.7255</td>\n",
       "      <td>0.9001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VAE-Softmax</th>\n",
       "      <td>0.9036</td>\n",
       "      <td>0.8814</td>\n",
       "      <td>0.6942</td>\n",
       "      <td>0.8700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Scenarios           Train+_Test+  Train+_Test-  Train-_Test+  Train-_Test-\n",
       "Model                                                                     \n",
       "Fully Connected           0.8739        0.8350        0.8695        0.8396\n",
       "LSTM                      0.9955        0.9955        0.9993        0.9993\n",
       "VAE-GenerateLabels        0.7255        0.9001        0.7255        0.9001\n",
       "VAE-Softmax               0.9036        0.8814        0.6942        0.8700"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_scenarios_display = all_scenarios.loc[:,[ 'Scenarios', 'F1 Score', 'Model']]\n",
    "all_scenarios_pivot = all_scenarios_display.pivot_table('F1 Score', 'Model', 'Scenarios')\n",
    "all_scenarios_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:52.358193Z",
     "start_time": "2017-07-24T01:21:46.312534Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb01ab35b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_and_save(\"All Results with Models in X Axis\",all_scenarios_pivot.plot(kind='bar', figsize=[10,4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-24T01:21:52.632596Z",
     "start_time": "2017-07-24T01:21:52.359832Z"
    }
   },
   "outputs": [],
   "source": [
    "#%%bash\n",
    "#zip -r result_plots.zip result_plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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